Towards Consistent State and Covariance Initialization for Monocular SLAM Filters

Tkocz, Marcel; Janschek, Klaus

doi:10.1007/s10846-015-0185-3

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Consequently, there is a variety of SLAM methods, and the development of a safety monitor that can be integrated with a majority of these methods may be impossible. Methods that allow the uncertainty estimation in SLAM have been proposed in the past [5], [6], [7], however this would require modifications in the internals of the SLAM algorithm, or, to obtain some internal matrices and do the computations externally.…”

Section: Introductionmentioning

confidence: 99%

DNN-based anomaly prediction for the uncertainty in visual SLAM

Bosdelekidis

Johansen

Sokolova

2022

2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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The method described in this paper proposes a supervised Deep Neural Network (DNN) approach for the prediction of anomalies in camera-based navigation. The method is inspired by the unsolved issues of Integrity Monitors (IMs) when some of the sensor measurement covariances are unknown or inconsistent. Especially, the focus is on predicting when the estimation error distribution would require fatter tails to include outliers. The developed method takes into account single-frame image features as well as transient changes in the error. In the best of our knowledge, this is the first work that predicts anomalies in the error covariance of SLAM estimates and associates them with low-level image features. Finally, the prediction method can be used with other sensors as well, allowing the future development of navigation algorithm-and sensor-agnostic safety monitoring frameworks.

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Section: Introductionmentioning

confidence: 99%

DNN-based anomaly prediction for the uncertainty in visual SLAM

Bosdelekidis

Johansen

Sokolova

2022

2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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An Obstacle Detection and Distance Measurement Method for Sloped Roads Based on VIDAR

Jiang

Gong

et al. 2022

Journal of Robotics

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Environmental perception systems can provide information on the environment around a vehicle, which is key to active vehicle safety systems. However, these systems underperform in cases of sloped roads. Real-time obstacle detection using monocular vision is a challenging problem in this situation. In this study, an obstacle detection and distance measurement method for sloped roads based on Vision-IMU based detection and range method (VIDAR) is proposed. First, the road images are collected and processed. Then, the road distance and slope information provided by a digital map is input into the VIDAR to detect and eliminate false obstacles (i.e., those for which no height can be calculated). The movement state of the obstacle is determined by tracking its lowest point. Finally, experimental analysis is carried out through simulation and real-vehicle experiments. The results show that the proposed method has higher detection accuracy than YOLO v5s in a sloped road environment and is not susceptible to interference from false obstacles. The most prominent contribution of this research work is to describe a sloped road obstacle detection method, which is capable of detecting all types of obstacles without prior knowledge to meet the needs of real-time and accurate detection of slope road obstacles.

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Towards Consistent State and Covariance Initialization for Monocular SLAM Filters

Cited by 2 publications

References 15 publications

DNN-based anomaly prediction for the uncertainty in visual SLAM

DNN-based anomaly prediction for the uncertainty in visual SLAM

An Obstacle Detection and Distance Measurement Method for Sloped Roads Based on VIDAR

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